Domain assistant system and method

ABSTRACT

A method, computer program product, and computer system identifying, by a computing device, an intent relating to a query associated with an object. A plurality of informational items associated with the object may be identified. At least a first informational item of the plurality of informational items associated with the object for which a first portion of information is already known may be identified. At least a second informational item of the plurality of informational items associated with the object for which a second portion of information is unknown may be identified. A question may be generated to determine the second portion of information based upon, at least in part, the second portion of information being unknown. An answer may be received for the question generated to determine the second portion of information. A response to the intent relating to the query may be provided based upon, at least in part, at least the first informational item for which the first portion of information is already known and the answer for the question generated to determine the second portion of information.

RELATED CASES

This application is a continuation of U.S. Pat. Application Serial No.16/965,526 filed on Jul. 28, 2020, which is a 371 of the PCT ApplicationNo. PCT/US2019/015413, filed on Jan. 28, 2019, which claims the benefitof U.S. Provisional Application No. 62/623,369, filed on 29 Jan. 2018,the contents of which are all incorporated by reference.

BACKGROUND

Question and Answer (Q&A) systems may be used, e.g., to help a usersolve problems or answer questions. Typically, a user asks a question(e.g., using a multimodal input) and the system attempts to gather theinformation needed to address the user’s question. Generally, standardconversational workflows use a fixed set of questions, in a specificorder, to get information from the user. That is, such Q&A systemstypically use a hard-coded path to a result where a fixed set of tasksare used for a set of fairly rigid conversational workflows to interactwith any user to collect needed information and to present results.

BRIEF SUMMARY OF DISCLOSURE

In one example implementation, a method, performed by one or morecomputing devices, may include but is not limited to identifying, by acomputing device, an intent relating to a query associated with anobject. A plurality of informational items associated with the objectmay be identified. At least a first informational item of the pluralityof informational items associated with the object for which a firstportion of information is already known may be identified. At least asecond informational item of the plurality of informational itemsassociated with the object for which a second portion of information isunknown may be identified. A question may be generated to determine thesecond portion of information based upon, at least in part, the secondportion of information being unknown. An answer may be received for thequestion generated to determine the second portion of information. Aresponse to the intent relating to the query may be provided based upon,at least in part, at least the first informational item for which thefirst portion of information is already known and the answer for thequestion generated to determine the second portion of information.

One or more of the following example features may be included. Theanswer for the question may be generated from a virtual agent. Asequence of a plurality of questions to be generated may be determined.The sequence of the plurality of questions to be generated may bedetermined based upon, at least in part, one of user information andcontext. The sequence of the plurality of questions to be generated maybe determined based upon, at least in part, a probability that thesecond portion of information is needed to provide the response to thequery. Determining the sequence of the plurality of questions to begenerated may include excluding generation of a second question fordetermining the first portion of information. A service for a secondobject associated with the object may be identified and one or moreadditional questions associated with the service may be generated. Thequestion may be one of a direct question and an indirect question todetermine the second portion of information.

In another example implementation, a computing system may include one ormore processors and one or more memories configured to performoperations that may include but are not limited to identifying an intentrelating to a query associated with an object. A plurality ofinformational items associated with the object may be identified. Atleast a first informational item of the plurality of informational itemsassociated with the object for which a first portion of information isalready known may be identified. At least a second informational item ofthe plurality of informational items associated with the object forwhich a second portion of information is unknown may be identified. Aquestion may be generated to determine the second portion of informationbased upon, at least in part, the second portion of information beingunknown. An answer may be received for the question generated todetermine the second portion of information. A response to the intentrelating to the query may be provided based upon, at least in part, atleast the first informational item for which the first portion ofinformation is already known and the answer for the question generatedto determine the second portion of information.

One or more of the following example features may be included. Theanswer for the question may be generated from a virtual agent. Asequence of a plurality of questions to be generated may be determined.The sequence of the plurality of questions to be generated may bedetermined based upon, at least in part, one of user information andcontext. The sequence of the plurality of questions to be generated maybe determined based upon, at least in part, a probability that thesecond portion of information is needed to provide the response to thequery. Determining the sequence of the plurality of questions to begenerated may include excluding generation of a second question fordetermining the first portion of information. A service for a secondobject associated with the object may be identified and one or moreadditional questions associated with the service may be generated. Thequestion may be one of a direct question and an indirect question todetermine the second portion of information.

In another example implementation, a computer program product may resideon a computer readable storage medium having a plurality of instructionsstored thereon which, when executed across one or more processors, maycause at least a portion of the one or more processors to performoperations that may include but are not limited to identifying an intentrelating to a query associated with an object. A plurality ofinformational items associated with the object may be identified. Atleast a first informational item of the plurality of informational itemsassociated with the object for which a first portion of information isalready known may be identified. At least a second informational item ofthe plurality of informational items associated with the object forwhich a second portion of information is unknown may be identified. Aquestion may be generated to determine the second portion of informationbased upon, at least in part, the second portion of information beingunknown. An answer may be received for the question generated todetermine the second portion of information. A response to the intentrelating to the query may be provided based upon, at least in part, atleast the first informational item for which the first portion ofinformation is already known and the answer for the question generatedto determine the second portion of information.

One or more of the following example features may be included. Theanswer for the question may be generated from a virtual agent. Asequence of a plurality of questions to be generated may be determined.The sequence of the plurality of questions to be generated may bedetermined based upon, at least in part, one of user information andcontext. The sequence of the plurality of questions to be generated maybe determined based upon, at least in part, a probability that thesecond portion of information is needed to provide the response to thequery. Determining the sequence of the plurality of questions to begenerated may include excluding generation of a second question fordetermining the first portion of information. A service for a secondobject associated with the object may be identified and one or moreadditional questions associated with the service may be generated. Thequestion may be one of a direct question and an indirect question todetermine the second portion of information.

The details of one or more example implementations are set forth in theaccompanying drawings and the description below. Other possible examplefeatures and/or possible example advantages will become apparent fromthe description, the drawings, and the claims. Some implementations maynot have those possible example features and/or possible exampleadvantages, and such possible example features and/or possible exampleadvantages may not necessarily be required of some implementations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example diagrammatic view of a query process coupled to anexample distributed computing network according to one or more exampleimplementations of the disclosure;

FIG. 2 is an example diagrammatic view of a client electronic device ofFIG. 1 according to one or more example implementations of thedisclosure;

FIG. 3 is an example flowchart of a query process according to one ormore example implementations of the disclosure;

FIG. 4 is an example diagrammatic view of a query process coupled to anexample distributed computing network according to one or more exampleimplementations of the disclosure;

FIG. 5 is an example diagrammatic view of a user interface according toone or more example implementations of the disclosure;

FIG. 6 is an example diagrammatic view of a graph according to one ormore example implementations of the disclosure;

FIG. 7 is an example diagrammatic view of a graph according to one ormore example implementations of the disclosure;

FIG. 8 is an example flowchart of a query process according to one ormore example implementations of the disclosure;

FIG. 9 is an example diagrammatic view of a graph according to one ormore example implementations of the disclosure;

FIG. 10 is an example diagrammatic view of a graph according to one ormore example implementations of the disclosure;

FIG. 11 is an example diagrammatic view of a graph according to one ormore example implementations of the disclosure; and

FIG. 12 is an example flowchart of a query process according to one ormore example implementations of the disclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION System Overview

In some implementations, the present disclosure may be embodied as amethod, system, or computer program product. Accordingly, in someimplementations, the present disclosure may take the form of an entirelyhardware implementation, an entirely software implementation (includingfirmware, resident software, micro-code, etc.) or an implementationcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore, insome implementations, the present disclosure may take the form of acomputer program product on a computer-usable storage medium havingcomputer-usable program code embodied in the medium.

In some implementations, any suitable computer usable or computerreadable medium (or media) may be utilized. The computer readable mediummay be a computer readable signal medium or a computer readable storagemedium. The computer-usable, or computer-readable, storage medium(including a storage device associated with a computing device or clientelectronic device) may be, for example, but is not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, device, or any suitable combination ofthe foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable medium may include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CD-ROM), anoptical storage device, a digital versatile disk (DVD), a static randomaccess memory (SRAM), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, a media such as those supportingthe internet or an intranet, or a magnetic storage device. Note that thecomputer-usable or computer-readable medium could even be a suitablemedium upon which the program is stored, scanned, compiled, interpreted,or otherwise processed in a suitable manner, if necessary, and thenstored in a computer memory. In the context of the present disclosure, acomputer-usable or computer-readable, storage medium may be any tangiblemedium that can contain or store a program for use by or in connectionwith the instruction execution system, apparatus, or device.

In some implementations, a computer readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. In someimplementations, such a propagated signal may take any of a variety offorms, including, but not limited to, electromagnetic, optical, or anysuitable combination thereof. In some implementations, the computerreadable program code may be transmitted using any appropriate medium,including but not limited to the internet, wireline, optical fibercable, RF, etc. In some implementations, a computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that can communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device.

In some implementations, computer program code for carrying outoperations of the present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java®, Smalltalk, C++ or the like.Java® and all Java-based trademarks and logos are trademarks orregistered trademarks of Oracle and/or its affiliates. However, thecomputer program code for carrying out operations of the presentdisclosure may also be written in conventional procedural programminglanguages, such as the “C” programming language, PASCAL, or similarprogramming languages, as well as in scripting languages such asJavascript, PERL, or Python. The program code may execute entirely onthe user’s computer, partly on the user’s computer, as a stand-alonesoftware package, partly on the user’s computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user’s computerthrough a local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theinternet using an Internet Service Provider). In some implementations,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGAs) or other hardwareaccelerators, micro-controller units (MCUs), or programmable logicarrays (PLAs) may execute the computer readable programinstructions/code by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

In some implementations, the flowchart and block diagrams in the figuresillustrate the architecture, functionality, and operation of possibleimplementations of apparatus (systems), methods and computer programproducts according to various implementations of the present disclosure.Each block in the flowchart and/or block diagrams, and combinations ofblocks in the flowchart and/or block diagrams, may represent a module,segment, or portion of code, which comprises one or more executablecomputer program instructions for implementing the specified logicalfunction(s)/act(s). These computer program instructions may be providedto a processor of a general purpose computer, special purpose computer,or other programmable data processing apparatus to produce a machine,such that the computer program instructions, which may execute via theprocessor of the computer or other programmable data processingapparatus, create the ability to implement one or more of thefunctions/acts specified in the flowchart and/or block diagram block orblocks or combinations thereof. It should be noted that, in someimplementations, the functions noted in the block(s) may occur out ofthe order noted in the figures (or combined or omitted). For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

In some implementations, these computer program instructions may also bestored in a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks or combinations thereof.

In some implementations, the computer program instructions may also beloaded onto a computer or other programmable data processing apparatusto cause a series of operational steps to be performed (not necessarilyin a particular order) on the computer or other programmable apparatusto produce a computer implemented process such that the instructionswhich execute on the computer or other programmable apparatus providesteps for implementing the functions/acts (not necessarily in aparticular order) specified in the flowchart and/or block diagram blockor blocks or combinations thereof.

Referring now to the example implementation of FIG. 1 , there is shownquery process 10 that may reside on and may be executed by a computer(e.g., computer 12), which may be connected to a network (e.g., network14) (e.g., the internet or a local area network). Examples of computer12 (and/or one or more of the client electronic devices noted below) mayinclude, but are not limited to, a storage system (e.g., a NetworkAttached Storage (NAS) system, a Storage Area Network (SAN)), a personalcomputer(s), a laptop computer(s), mobile computing device(s), a servercomputer, a series of server computers, a mainframe computer(s), or acomputing cloud(s). As is known in the art, a SAN may include one ormore of the client electronic devices, including a RAID device and a NASsystem. In some implementations, each of the aforementioned may begenerally described as a computing device. In certain implementations, acomputing device may be a physical or virtual device. In manyimplementations, a computing device may be any device capable ofperforming operations, such as a dedicated processor, a portion of aprocessor, a virtual processor, a portion of a virtual processor,portion of a virtual device, or a virtual device. In someimplementations, a processor may be a physical processor or a virtualprocessor. In some implementations, a virtual processor may correspondto one or more parts of one or more physical processors. In someimplementations, the instructions/logic may be distributed and executedacross one or more processors, virtual or physical, to execute theinstructions/logic. Computer 12 may execute an operating system, forexample, but not limited to, Microsoft® Windows®; Mac® OS X®; Red Hat®Linux®, Windows® Mobile, Chrome OS, Blackberry OS, Fire OS, or a customoperating system. (Microsoft and Windows are registered trademarks ofMicrosoft Corporation in the United States, other countries or both; Macand OS X are registered trademarks of Apple Inc. in the United States,other countries or both; Red Hat is a registered trademark of Red HatCorporation in the United States, other countries or both; and Linux isa registered trademark of Linus Torvalds in the United States, othercountries or both).

In some implementations, as will be discussed below in greater detail, aquery process, such as query process 10 of FIG. 1 , may identify anintent relating to a query associated with an object. A plurality ofinformational items associated with the object may be identified. Atleast a first informational item of the plurality of informational itemsassociated with the object for which a first portion of information isalready known may be identified. At least a second informational item ofthe plurality of informational items associated with the object forwhich a second portion of information is unknown may be identified. Aquestion may be generated to determine the second portion of informationbased upon, at least in part, the second portion of information beingunknown. An answer may be received for the question generated todetermine the second portion of information. A response to the intentrelating to the query may be provided based upon, at least in part, atleast the first informational item for which the first portion ofinformation is already known and the answer for the question generatedto determine the second portion of information.

In some implementations, the instruction sets and subroutines of queryprocess 10, which may be stored on storage device, such as storagedevice 16, coupled to computer 12, may be executed by one or moreprocessors and one or more memory architectures included within computer12. In some implementations, storage device 16 may include but is notlimited to: a hard disk drive; all forms of flash memory storagedevices; a tape drive; an optical drive; a RAID array (or other array);a random access memory (RAM); a read-only memory (ROM); or combinationthereof. In some implementations, storage device 16 may be organized asan extent, an extent pool, a RAID extent (e.g., an example 4D+1P R5,where the RAID extent may include, e.g., five storage device extentsthat may be allocated from, e.g., five different storage devices), amapped RAID (e.g., a collection of RAID extents), or combinationthereof.

In some implementations, network 14 may be connected to one or moresecondary networks (e.g., network 18), examples of which may include butare not limited to: a local area network; a wide area network or othertelecommunications network facility; or an intranet, for example. Thephrase “telecommunications network facility,” as used herein, may referto a facility configured to transmit, and/or receive transmissionsto/from one or more mobile client electronic devices (e.g., cellphones,etc.) as well as many others.

In some implementations, computer 12 may include a data store, such as adatabase (e.g., relational database, object-oriented database,triplestore database, etc.) and may be located within any suitablememory location, such as storage device 16 coupled to computer 12. Insome implementations, data, metadata, information, etc. describedthroughout the present disclosure may be stored in the data store. Insome implementations, computer 12 may utilize any known databasemanagement system such as, but not limited to, DB2, in order to providemulti-user access to one or more databases, such as the above notedrelational database. In some implementations, the data store may also bea custom database, such as, for example, a flat file database or an XMLdatabase. In some implementations, any other form(s) of a data storagestructure and/or organization may also be used. In some implementations,query process 10 may be a component of the data store, a standaloneapplication that interfaces with the above noted data store and/or anapplet / application that is accessed via client applications 22, 24,26, 28. In some implementations, the above noted data store may be, inwhole or in part, distributed in a cloud computing topology. In thisway, computer 12 and storage device 16 may refer to multiple devices,which may also be distributed throughout the network.

In some implementations, computer 12 may execute a dialogue managementapplication (e.g., dialogue management application 20), examples ofwhich may include, but are not limited to, e.g., a virtual assistantapplication, a domain assistant application, a query application, adatastore/knowledge base application, a service fulfillment application,an automatic speech recognition (ASR) application, examples of which mayinclude, but are not limited to, e.g., an automatic speech recognition(ASR) application (e.g., modeling, etc.), a natural languageunderstanding (NLU) application (e.g., machine learning, intentdiscovery, etc.), a text to speech (TTS) application (e.g., contextawareness, learning, etc.), a speech signal enhancement (SSE)application (e.g., multi-zone processing/beamforming, noise suppression,etc.), a voice biometrics/wake-up-word processing application, a websiteapplication, an Instant Messaging (IM)/“chat” application, a shortmessaging service (SMS)/multimedia messaging service (MMS) application,or other application that allows for receiving and/or responding to auser’s computer based query. In some implementations, query process 10and/or dialogue management application 20 may be accessed via one ormore of client applications 22, 24, 26, 28. In some implementations,query process 10 may be a standalone application, or may be an applet /application / script / extension that may interact with and/or beexecuted within dialogue management application 20, a component ofdialogue management application 20, and/or one or more of clientapplications 22, 24, 26, 28. In some implementations, dialoguemanagement application 20 may be a standalone application, or may be anapplet / application / script / extension that may interact with and/orbe executed within query process 10, a component of query process 10,and/or one or more of client applications 22, 24, 26, 28. In someimplementations, one or more of client applications 22, 24, 26, 28 maybe a standalone application, or may be an applet / application / script/ extension that may interact with and/or be executed within and/or be acomponent of query process 10 and/or dialogue management application 20.Examples of client applications 22, 24, 26, 28 may include, but are notlimited to, e.g., a virtual assistant application, a domain assistantapplication, a query application, a datastore/knowledge baseapplication, a service fulfillment application, ASR application, awebsite application, an Instant Messaging (IM)/“chat” application, ashort messaging service (SMS)/multimedia messaging service (MMS)application, or other application that allows for receiving and/orresponding to a user’s computer based query, a standard and/or mobileweb browser, an email application (e.g., an email client application), atextual and/or a graphical user interface, a customized web browser, aplugin, an Application Programming Interface (API), or a customapplication. The instruction sets and subroutines of client applications22, 24, 26, 28, which may be stored on storage devices 30, 32, 34, 36,coupled to client electronic devices 38, 40, 42, 44, may be executed byone or more processors and one or more memory architectures incorporatedinto client electronic devices 38, 40, 42, 44.

In some implementations, one or more of storage devices 30, 32, 34, 36,may include but are not limited to: hard disk drives; flash drives, tapedrives; optical drives; RAID arrays; random access memories (RAM); andread-only memories (ROM). Examples of client electronic devices 38, 40,42, 44 (and/or computer 12) may include, but are not limited to, apersonal computer (e.g., client electronic device 38), a laptop computer(e.g., client electronic device 40), a smart/data-enabled, cellularphone (e.g., client electronic device 42), a notebook computer (e.g.,client electronic device 44), a tablet, a server, a television, a smarttelevision, a smart speaker, an Internet of Things (IoT) device, a media(e.g., audio/video, photo, etc.) capturing and/or output device, and adedicated network device. Client electronic devices 38, 40, 42, 44 mayeach execute an operating system, examples of which may include but arenot limited to, Android™, Apple® iOS®, Mac® OS X®; Red Hat® Linux®,Windows® Mobile, Chrome OS, Blackberry OS, Fire OS, or a customoperating system.

In some implementations, one or more of client applications 22, 24, 26,28 may be configured to effectuate some or all of the functionality ofquery process 10 (and vice versa). Accordingly, in some implementations,query process 10 may be a purely server-side application, a purelyclient-side application, or a hybrid server-side / client-sideapplication that is cooperatively executed by one or more of clientapplications 22, 24, 26, 28 and/or query process 10.

In some implementations, one or more of client applications 22, 24, 26,28 may be configured to effectuate some or all of the functionality ofdialogue management application 20 (and vice versa). Accordingly, insome implementations, dialogue management application 20 may be a purelyserver-side application, a purely client-side application, or a hybridserver-side / client-side application that is cooperatively executed byone or more of client applications 22, 24, 26, 28 and/or dialoguemanagement application 20. As one or more of client applications 22, 24,26, 28, query process 10, and dialogue management application 20, takensingly or in any combination, may effectuate some or all of the samefunctionality, any description of effectuating such functionality viaone or more of client applications 22, 24, 26, 28, query process 10,dialogue management application 20, or combination thereof, and anydescribed interaction(s) between one or more of client applications 22,24, 26, 28, query process 10, dialogue management application 20, orcombination thereof to effectuate such functionality, should be taken asan example only and not to limit the scope of the disclosure.

In some implementations, one or more of users 46, 48, 50, 52 may accesscomputer 12 and query process 10 (e.g., using one or more of clientelectronic devices 38, 40, 42, 44) directly through network 14 orthrough secondary network 18. Further, computer 12 may be connected tonetwork 14 through secondary network 18, as illustrated with phantomlink line 54. Query process 10 may include one or more user interfaces,such as browsers and textual or graphical user interfaces, through whichusers 46, 48, 50, 52 may access query process 10.

In some implementations, the various client electronic devices may bedirectly or indirectly coupled to network 14 (or network 18). Forexample, client electronic device 38 is shown directly coupled tonetwork 14 via a hardwired network connection. Further, clientelectronic device 44 is shown directly coupled to network 18 via ahardwired network connection. Client electronic device 40 is shownwirelessly coupled to network 14 via wireless communication channel 56established between client electronic device 40 and wireless accesspoint (i.e., WAP) 58, which is shown directly coupled to network 14. WAP58 may be, for example, an IEEE 802.11a, 802.11b, 802.11 g, 802.11n,802.11ac, Wi-Fi®, RFID, and/or Bluetooth™ (including Bluetooth™ LowEnergy) device that is capable of establishing wireless communicationchannel 56 between client electronic device 40 and WAP 58. Clientelectronic device 42 is shown wirelessly coupled to network 14 viawireless communication channel 60 established between client electronicdevice 42 and cellular network / bridge 62, which is shown by exampledirectly coupled to network 14.

In some implementations, some or all of the IEEE 802.11x specificationsmay use Ethernet protocol and carrier sense multiple access withcollision avoidance (i.e., CSMA/CA) for path sharing. The various802.11x specifications may use phase-shift keying (i.e., PSK) modulationor complementary code keying (i.e., CCK) modulation, for example.Bluetooth™ (including Bluetooth™ Low Energy) is a telecommunicationsindustry specification that allows, e.g., mobile phones, computers,smart phones, and other electronic devices to be interconnected using ashort-range wireless connection. Other forms of interconnection (e.g.,Near Field Communication (NFC)) may also be used.

In some implementations, various I/O requests (e.g., I/O request 15) maybe sent from, e.g., client applications 22, 24, 26, 28 to, e.g.,computer 12. Examples of I/O request 15 may include but are not limitedto, data write requests (e.g., a request that content be written tocomputer 12) and data read requests (e.g., a request that content beread from computer 12).

Referring also to the example implementation of FIG. 2 , there is showna diagrammatic view of client electronic device 38. While clientelectronic device 38 is shown in this figure, this is for examplepurposes only and is not intended to be a limitation of this disclosure,as other configurations are possible. Additionally, any computing devicecapable of executing, in whole or in part, query process 10 may besubstituted for client electronic device 38 (in whole or in part) withinFIG. 2 , examples of which may include but are not limited to computer12 and/or one or more of client electronic devices 38, 40, 42, 44.

In some implementations, client electronic device 38 may include aprocessor (e.g., microprocessor 200) configured to, e.g., process dataand execute the above-noted code / instruction sets and subroutines.Microprocessor 200 may be coupled via a storage adaptor to theabove-noted storage device(s) (e.g., storage device 30). An I/Ocontroller (e.g., I/O controller 202) may be configured to couplemicroprocessor 200 with various devices (e.g., via wired or wirelessconnection), such as keyboard 206, pointing/selecting device (e.g.,touchpad, touchscreen, mouse 208, etc.), custom device (e.g.,microphone/speaker 215), USB ports, and printer ports. A display adaptor(e.g., display adaptor 210) may be configured to couple display 212(e.g., touchscreen monitor(s), plasma, CRT, or LCD monitor(s), etc.)with microprocessor 200, while network controller/adaptor 214 (e.g., anEthernet adaptor) may be configured to couple microprocessor 200 to theabove-noted network 14 (e.g., the Internet or a local area network).

As will be discussed below, query process 10 may at least help, e.g.,improve a technology necessarily rooted in computer technology in orderto overcome an example and non-limiting problem specifically arising inthe realm of computer systems associated with, e.g., the practicalapplication of receiving and/or responding to a user’s query. It will beappreciated that the computer processes described throughout areintegrated into one or more practical applications, and when taken atleast as a whole are not considered to be well-understood, routine, andconventional functions.

The Query Process

As discussed above and referring also at least to the exampleimplementations of FIGS. 3-11 , query process 10 may identify 300 anintent relating to a query associated with an object. Query process 10may identify 302 a plurality of informational items associated with theobject. Query process 10 may identify 304 at least a first informationalitem of the plurality of informational items associated with the objectfor which a first portion of information is already known. Query process10 may identify 306 at least a second informational item of theplurality of informational items associated with the object for which asecond portion of information is unknown. Query process 10 may generate308 a question to determine the second portion of information basedupon, at least in part, the second portion of information being unknown.Query process 10 may receive 310 an answer for the question generated todetermine the second portion of information. Query process 10 mayprovide 312 a response to the intent relating to the query based upon,at least in part, at least the first informational item for which thefirst portion of information is already known and the answer for thequestion generated to determine the second portion of information.

In some implementations, query process 10 may be integrated with (orhave access to) an example domain assistant system. For example,referring at least to the example implementation of FIG. 4 , an exampledomain assistant system 400 is shown. It will be appreciated that domainassistant system 400 may have more or fewer components, components thatmay be combined with various components, and have other configurations,such as being integrated into the environment of FIG. 1 , withoutdeparting from the scope of the present disclosure. As such, thespecific configuration and components shown in FIG. 4 should be taken asexample only and not to otherwise limit the scope of the disclosure. Thedomain assistant 400 may be generally defined as a system that assists auser in addressing different needs in a particular field (e.g.,automotive industry field) or multiple fields. Domain assistant 400 mayassist the user in addressing one or more needs through one or moreservice domains. This may be accomplished by, e.g., accessing answers(e.g., information) to questions that are received or obtained, but mayalso include executing one or more predetermined tasks (e.g., actions)after a request for such a task(s) has been received. A request by theuser may refer to any type of request (e.g., task to be completed,request for information obtained from query, etc.) and a response mayrefer to any type of response from the system (e.g., executed task typeof response, information provided type of response, etc.). Domainassistant 400 may assist with other types of requests and/or responseswithout departing from the scope of the disclosure.

In some implementations, domain assistant system may include, forexample, an understanding module, a conversation query manager (CQM),and a knowledge module. In some implementations, the two main parts usedto drive the workflow of the domain assistant system may include, e.g.,the understanding module and the knowledge module. The CQM (which insome implementations may be a component of query process 10) may be usedto translate messages and other information into proper formats betweenthe understanding module and the knowledge module.

In some implementations, this may form a loop e.g., user informationfrom client electronic device 42 may be input to the understandingmodule (which in some implementations may be a component of queryprocess 10) and may be routed through the CQM to get an answer from theknowledge module (which in some implementations may be a component ofquery process 10), the answer may come back through the CQM, and theunderstanding module may process the answer and may send the answer tothe user in a way that makes sense to the user. In some implementations,the user (e.g., user 50) may provide input to query process 10 using anyconventional technique and/or user interface (e.g., speech, text, etc.).

Similarly, the understanding module may have a multimodal input that mayinclude speech, text, or another type of input. In some implementations,the understanding module may include, e.g., ASR, NLU, and a DialogManager (DM) (each of which in some implementations may be a componentof query process 10). Using at least some of these components may helpthe understanding module determine and understand what is beingrequested by the user. Generally, but without limitation, the ASR maysend recognized speech to the NLU, which may send a determined intent ofthe user request to the DM. Generally, but without limitation, speechinput from the user may be directed to the ASR, text input may bedirected to the NLU, and application user interface (UI) input may bedirected to DM.

In some implementations, the understanding module (e.g., via queryprocess 10) may provide various example functionalities. For example,the understanding module may interpret the user input (e.g., understandhow to communicate with the user such as understanding English, please,thank you, etc.). The understanding module may drive actions to providenext steps of the domain assistant that may run a service workflow, suchas a configuration workflow with steps that may provide an answer to theuser’s query. The understanding module may convert the user input into arequest to the knowledge module and may interpret answers from theknowledge module (e.g., understanding what the user meant and presentingresults in a manner understandable to the user). Workflow may be mappedexplicitly or generated dynamically, dynamically possibly including goalplanning/route optimization/optimization of existing workflow. Theunderstanding module may provide answers to the user (e.g., next step ofservice workflow such as a configuration workflow). In someimplementations, the understanding module may include, e.g., atext-to-speech (TTS) module and a natural language generation (NLG)module (each of which in some implementations may be a component ofquery process 10), and may be used depending on the output type providedto the user (e.g., NLG for text and TTS for speech).

In some implementations, the understanding module may store and make useof previous user inputs, from the current or prior conversations withthe user, in order to better understand the user’s current input. As aresult of tracking the user’s input, the understanding module may knowwhere in the workflow a user is at any time, and may keep track of thisas the conversation continues. Additionally, the understanding modulemay also store other information in a user KB (e.g., location of theuser, user preferences, etc.).

In some implementations, the understanding module may look for answersby gathering knowledge from the knowledge module via the CQM. Theunderstanding module may set up the query and may then use the CQM toconvert the query to pull relevant knowledge or information from theknowledge module. For example, the CQM may provide examplefunctionalities (e.g., via query process 10). For instance, the CQM mayprovide translating functionality (e.g., transform a DM query from theunderstanding module into a knowledge query such as knowledge base (KB)query or user KB query (e.g., using cypher, SQL, etc.)). The CQM mayformat answers from the knowledge module for consumption by the DM ofthe understanding module, and may enable the understanding module andknowledge module to communicate with each other. In someimplementations, the CQM may be designed specific to each knowledgemodule based on one or more knowledge bases (KBs) and/or one or moredatabases (DBs) and/or one or more user KBs of the knowledge module.

In some implementations, the knowledge module may include one or moreKBs and/or one or more databases DBs and/or one or more user KBsrelating to one or more domains. Generally, the KB and user KB may becapable of encoding complex structured and unstructured data, whereas atable-based DB (or other DB type) may be more suited to encodestructured information. Typically, a KB may be a graph model and a DBmay be table-oriented, document oriented, or key value store; however,it will be appreciated that other configurations are possible. Userknowledge may or may not be integrated into the user KB. For example,user knowledge may be integrated into the user KB or commingled withother data (e.g., organization data such as company data) in another KBor DB. As such, it will be understood that the user knowledge may befound in other data source arrangements or configurations withoutdeparting from the scope of the disclosure.

In some implementations, the knowledge module may include information onone or more service domains (e.g., configuration domains), such aseverything to know about printers, tractors, machines, etc. In someimplementations, the knowledge module may include information aboutdependencies between options (e.g., compatibility). For instance, theknowledge module may be able to determine whether a particular component(e.g., ink cartridge) is compatible with a system such as a particularprinter (e.g., yes or no). Similarly, the knowledge module may haveinformation associated with the requirements for a requested option(e.g., user wants to install a particular component and the requirementsmay include (1) order the component, (2) receive the component, (3)receive instructions on how to install the component, and query process10 may determine next steps).

In some implementations, given a particular workflow step, the knowledgemodule may have information associated with the next step that needs tobe performed (e.g., if the user is at position in the workflow to selecta component such as an item to be ordered, then, if user orders it, thenext step may be that user will receive a confirmation, after which thenthe query process 10 may contact the user by email about how to trackpackage that includes the ordered item, etc.).

In some implementations, the knowledge module may have informationassociated with configuration elements and their properties. Forexample, the knowledge module may have information about what isconfigurable and the limits required in a configuration (e.g., acomponent such as a specific bucket has width and capacity and thebucket may be mounted on some machines but not mounted on othermachines, etc.).

In some implementations, the user knowledge base may have informationassociated with the user, independent of their interaction in thecurrent conversation. For example, the user KB may know a user’sorganization, job function, location, position in the organizationstructure, etc. and this may inform the interpretation of the user’sinput or of the response by query process 10. For instance, if the userrequests a summary of his or her sales figures, query process 10 maydetermine which sales should be included in those results based oninformation in the user KB. As another example, if a user works in acertain industry, that knowledge could inform the selection ofappropriate equipment options when configuring their machinery.

As described above, the domain assistant may be realized by queryprocess 10 as shown in FIG. 4 . In some examples, the domain assistantmay be implemented in a hierarchical manner, e.g., trigger specificdomain services to accomplish subparts of a user request. For example,the domain assistant may run query process 10 (or vice versa) such thatquery sub-processes (corresponding to domain services) are allimplemented as shown in FIG. 4 . For example, each query sub-process maycorrespond with a domain service (e.g., configuration service orconfiguration service). In other examples, multiple query sub-processesmay correspond with a domain service.

In some implementations, the domain assistant system may include, forexample, a service execution module (which in some implementations maybe a component of query process 10) that may satisfy a broad set of userrequests relating to one or more services (domain services). Forexample, the service execution module may be used for taking care ofactions related to a particular service. The service execution module ofthe query process triggers at least one query sub-process (relating to aspecific domain service). For example, the DM may issue instructions tothe service execution module directing the service execution module totrigger or initiate at least one query sub-process. The serviceexecution module may also implement software programmable tasks withrespect to one or more specific services (e.g., place an order, create arecord on database of orders, set a reminder for a machine’s scheduledmaintenance on the owner’s digital calendar, etc.).

Thus, as a brief summary, the DM of the understanding module may drivetransformation from natural language to a CQM query. The DM of theunderstanding module may produce a DM information/data request to betargeted for a database (DB) of the knowledge module. The CQM maytranslate the high-level DM information request from the understandingmodule to one or more query languages as addressed to the KB and/or DBand/or user KB of the knowledge module. With respect to configurationservice, the KB (e.g., graph database) of the knowledge module may haveencoded compatibility options in a way so that a next configurationoption may be deduced using, e.g., node information queries, inferencelogic or graph traversals (e.g., based on a query request from the DM).The knowledge module may send the answers (based on information queriesfrom DM) back to the CQM, which in turn may format the answer for DMconsumption to allow the understanding module to drive the next userinteraction (e.g., interaction may be multi-modal such that output maybe voice-related using NLG/TTS, displayed on screen, provided as text,or any other output described throughout or known to those skilled inthe art, etc.). At certain points in the process, and based on requiredinformation, tasks may be triggered though the service execution module.It will be appreciated that the functionalities described above andthroughout may be integrated into more, fewer and/or differentcomponents. As such, the specific interactions described between variouscomponents should be taken as example only and not to otherwise limitthe scope of the present disclosure.

As will be discussed below, query process 10 may have both proactive andinteractive capabilities. For example, interactive capabilities maygenerally be described as being primarily active when the user isactively talking or otherwise interacting with query process 10, whereasthe proactive capabilities may generally be described as monitoringdomain and external knowledge that is accessible (e.g., visible).Depending on context/areas of responsibility, query process 10 maytrigger an action, where that action may or may not include, e.g.,establishing contact/speaking to the user and thus converting aproactive action into an interactive one, record keeping, publication toother systems, triggering workflows, etc.

In an example summary, query process 10 may generally involve having acommand or query issued by a user (e.g., based on an intent) and havingquery process 10 attempt to interpret that intent and respondappropriately. There may be different types of “triggers” that indicatethe existence of an intent, such as changes in the world state(including the passage of, or reaching of, a certain time), actionstaken by other agents or systems, conditions on available data (e.g.,some value exceeding a threshold), or conversational actions or states(e.g., the user repeatedly asking for clarification). The intent maytypically include some type of goal. After the goal is satisfied, thedomain assistant may identify a related or follow-on goal (e.g., an“up-sell” opportunity after the user has purchased some good or service)as will be discussed further below.

There are different ways for intent to be identified. For example, thedomain assistant (e.g., via query process 10) may obtain intent from theuser by: (1) getting a command from the user (from which the system isexpected to determine the user’s intent), and (2) getting additionalinformation needed to flush out the intent (e.g., intent parameters(entities) that may not have been specified initially). The domainassistant may also obtain information to complete a fulfillment of thatintent. As an example, suppose a user wishes to book a plane trip. Theremay be on the order of a dozen parameters that are required to selectthe appropriate flight and it is highly unlikely that the user willspecify all of them in the initial request. If the domain assistantaccepts that an initial intent specification need not include all of thevalues needed to satisfy the intent, then it becomes somewhat arbitraryas to which parameters should be considered as part of the intentsignature and which will be acquired as part of intent satisfaction. Forexample, any of the following may be considered a legitimate userintent:

-   I’d like to buy a bucket.-   I’d like to buy a 48″ bucket.-   I’d like to buy a heavy-duty bucket.-   I’d like to buy a heavy-duty 48″ bucket.-   I’d like to buy a heavy-duty 48″ bucket for use with a pin grabber    coupler.-   I’d like to buy a heavy-duty 48″ bucket with a bolt-o cutting edge    for use with a pin grabber coupler.

In some implementations, query process 10, as part of its generalprocessing flow, may include one or more capabilities, e.g.,: theability to identify information it needs and the ability to identify andprioritize sources for that information. Given that capability, queryprocess 10 may not need to request information from the user if it has apreferable source for it, where that preference may be based on a numberof factors. Thus, if the needed information is already available “inmemory” or from an easily accessed data source, those may typically beused rather than invoking a query to the user.

In some implementations, query process 10 may identify 300 an intentrelating to a query associated with an object. For instance, assume forexample purposes only that a user (e.g., user 50) is interacting withquery process 10 via an example and non-limiting user interface, such asUI 500 shown in the example implementation of FIG. 5 . In the example,UI 500 may enable user 50 to interact with, e.g., a virtual agent orother type of interactive system. In some implementations, once user 50has started an interactive session, query process 10 may identify 300the user’s intent (e.g., an action or activity the user wishes to invokeor a state the user intends to achieve) relating to a query associatedwith an object. In the case of an interface where user 50 may physicallyselect an item/object, this may be a definitive indication of theintent. However, speaking/typing in natural language may require queryprocess 10 to recognize the language and interpret the intent, where theintent may be identified (for example) from any combination of thefollowing: The user’s spoken or written utterance, the user’sinteraction with visual/interactive elements of an application (such asUI 500), the user’s context that exists up to that point in time (e.g.,past actions, history of user such what was said, location of user,users preferences, attributes, organizational knowledge, where in the UIis the user currently, knowledge available for user such as customerrelationship management (CRM-type) knowledge etc.), domain context(e.g., any other assumptions about the domain associated with the user’squery that would help identify intent), directly based on what was askedfor and/or interacted with, as well as indirectly based on theabove-noted history and context.

In the example shown at FIG. 5 , assume for example purposes only thatuser 50 uses UI 500 to say, “Can you find me a fork for my skid steer.”In the example, the intent may be derived directly from what the usersays (i.e., user 50 has the intent of needing something for his systemsuch as skid steer, where the object of the intent is the component suchas fork which may be requested in a query). If the intent is clear,query process 10 may be able to determine the appropriate service.However, assume instead for example purposes only that user 50 says, “Ineed to find some parts.” In this case, where the intent is unclear orambiguous, query process 10 may ask for clarification or more detail, orpresent options to simplify the process of identifying intent, whichwill be discussed further below.

As described above, query process 10 may be triggered by a variety ofsignals, e.g., either user-initiated actions (interactive) or based oninformation provided by background processes (proactive). Changes to aworld model of query process 10 may cause it to identify potentiallyrelevant user intents and system activities. For example, a publicationof a service bulletin may be recognized as relevant to a user’s goal ofmaintaining their equipment and may trigger an action of providing thatinformation to the user. Further, an intent or goal may be determinedfrom known user actions, user dialogue, events, and/or user-initiatedactions. Recognition of a user’s intent may, itself, hint at otherpossible intents. For example, the purchase of a piece of equipment mayestablish goals for subsequent maintenance, servicing, or insuring ofthat equipment (as will be discussed further below). In another example,query process 10 may monitor activity relating to a service work on theuser’s system such as equipment (e.g., tractor) indicating that acomponent (or part) is needed for repair (e.g., a gear broke and needsto be replaced, thus suggesting intent or goal of purchasing a newgear). In some implementations, query process 10 may monitor activityindicating that a component needs to be replaced for a system due to newsafety rules or other legal regulations impacting the system, thushinting at an intent to purchase a replaceable component. Wherepotential intents or goals are determined indirectly, query process 10may confirm the intent with the user, e.g., via UI 500.

In some implementations, each intent (e.g., goal) type may correspond toeither a set of utterances (where this mapping is learned by queryprocess 10 via training data), an interaction with the application userinterface, or by a set of conditions that indicate the goal state. Aftereach user interaction or state change, query process 10 may attempt toidentify matching (or at least potentially matching) intents and maythen make additional queries, either to the user or to other systems ordata sources, to confirm the intent.

In some implementations, the user’s action or state change may besufficient to imply one or more potential intents but may not provideenough information for the intent to be fully specified. As a simpleexample, query process 10 may recognize that the user wishes to travelto Chicago, but may not yet know the desired travel date. Byunderstanding what information is required to fully specify each intent,query process 10 may proceed to obtain the additional neededinformation. As will be discussed further below, this may involvefurther interactions with the user, data lookups, or invocations ofother systems or services. In some implementations, if query process 10already has or can obtain this information, it may avoid unnecessaryinteractions (e.g., asking questions) with the user, or may choose topresent the information being known for validation by the user.

In some implementations, assuming that query process 10 has identifiedone more user intents, it may select an appropriate service to satisfythat intent (e.g., selected and executed via service execution module).Different services may have different capabilities, approaches toworking with the user, and in many cases different sources/algorithmsfrom which to pull data. These multiple services may have a flexibledegree of connection to query process 10. For example, the services maybe part of the domain assistant such that each service may triggersubsequent dedicated service-specific query sub-processes (e.g., usingservice execution module). The query sub-processes may have a flexibledegree of connection to query process 10. In another example, theservices (e.g., including service-specific processes) may be looselycoupled, integrated, and/or independent from query process 10 or evenspecific data sources accessible by query process 10 (and may beexecuted, e.g., using the service execution module).

In some implementations, query process 10 may have expertise in itsunderstanding/interactivity/capabilities as personified by each service,which may work in concert to deliver a functional experience to theuser. An instance of query process 10 may be configured so that aparticular service is associated with certain intent classifications andthis may be context dependent (e.g., when the user asks about a book,that could be addressed by a service such as a librarian service, whenthe user asks about a product, that could be addressed by a service suchas a shopping service).

In some implementations, to aid in the effectiveness of this process, amodel of the intent space may be created. This may be a representationof, e.g., what query process 10 determines may be of use to the user,how the user may approach the knowledge of the domain, what informationexists within the domain, and the relationships between the differenttypes of queries and/or actions within the domain. This may be a closematch to the knowledge model of the system or require a mapping layer tohelp bridge an understanding of expectations of users as compared to theknowledge of the world.

In some implementations, query process 10 may identify 302 a pluralityof informational items associated with the object. For example,identifying a user’s intent may be necessary to satisfy the user’s goal,but it may not be sufficient to execute the proper query or for theservice to complete the goal. Typically, additional information may berequired and this information may be obtained from a variety of sources.Thus, it may be required to identify the required information (e.g., theinformational items needed to complete the proper query/service), andobtain this information in an efficient and effective manner. In someimplementations, the informational items needed to determine and/orcomplete the proper query/service workflow may include, e.g.,information needed to identify one or more objects (e.g., hammers for askid steer, transmission for car, buckets for an excavator, etc.),information needed to identify the service and/or the domain associatedwith the object (e.g., automotive parts).

The identification of the required informational items may occur inmultiple ways. For example, each intent usually specifies a set ofparameters that are required to fully specify that intent. For instance,the intent of ordering a component such as a part is usually notsufficient unless there is some indication of the part type to beordered. Similarly, booking a flight may require, at a minimum, knowingthe origin and destination cities or airports. Thus, the definition ofthe intents themselves may identify some of the key informational itemsneeded.

In some implementations, query process 10 may identify 304 at least afirst informational item of the plurality of informational itemsassociated with the object for which a first portion of information isalready known. For instance, and continuing with the above example, thefirst informational item (e.g., which machine class such as skid steerclass) may be associated with the first portion of information (e.g.,skid steer class, model number such as 505e, another product ID for skidsteer class) to identify the object of the user’s query intent (e.g., ifthe user were to express “I need a new hammer for my skid steer”). Thus,in the example, the first portion of information of the informationalitem(s) may already be known. In some implementations, the“informational items” (may also be referred to as “requiredinformation”) are needed for finding appropriate object. For example,what are at least two “informational items” that need to be known toidentify a compatible hammer. The “portion of information” is the actualinformation used for determining the “informational item.” The “portionof information” may be essentially the same as “informational item” ormay be tangential information that query process 10 may use to identifythe “informational item” based on correlations. For example, if the“informational item” is “type of tractor” then either the “portion ofinformation” may be “backhoe loader” (type of tractor) or tangentialinformation, e.g., “model number”, which is thus linked to a “backhoeloader”. In other words, there may be situations where the informationneeded (informational item) is the same as what the system has, and/ormay also cover other situations where the system does not have theneeded information, but has other information tangentially related suchthat system can identify what information (portion of information) canbe used to determine the informational item. As another example, thefirst portion of information may the same as the first informationalitem (e.g., first informational item may be a car make/model and firstportion of information is Toyota Camry). In another example, the firstportion of information may be tangential or indirect information thatthe query process 10 may use to identify first informational item (e.g.,first informational item may be a car model and first portion ofinformation may be a vehicle identification number (VIN), model number,or another identification number that may be used to identify carmodel). In another example, tangential or indirect information for thefirst portion of information may refer to correlations (e.g., firstinformational item may be a car model and first portion of informationmay be any Toyota car-related information that may be used to identifyToyota Camry as the car model through correlations such as engine type,car class, etc.)

In some implementations, query process 10 may identify 306 at least asecond informational item of the plurality of informational itemsassociated with the object for which a second portion of information isunknown. For instance, and continuing with the above example, assumequery process 10 has identified the first informational item (e.g., whatis the class of machine) and the first portion of information (e.g.,what model skid steer). In the example, query process 10 may now need toidentify a second information item (e.g., hydraulic power output ofmachine), which the second portion of information (e.g., hydraulic poweroutput, or whether the skid steer has advanced auxiliary hydraulics)needed to answer this question may be currently unknown. In someimplementations, further informational needs may be specified dependingon how the intent gets satisfied. For example, when looking for areplacement component or part (e.g., hammer), the solution path mayinclude determining component compatibility with the user’s existingsystem such as equipment (e.g., skid steer). If such a path is taken,there may be a need to identify the existing system configuration (e.g.,ask what other work tools may be in the user’s possession to determinemachine features), which query process 10 may ask a question todetermine the unknown second portion of informational. Similar to above,in an example, the second portion of information may be the same as thesecond informational item (e.g., second informational item may behydraulic power output of machine and second portion of information maybe hydraulic power output which may be a power range). In anotherexample, the second portion of information may be tangential or indirectinformation that query process 10 may use to determine secondinformational item (e.g., the second informational item may be hydraulicpower output of machine and second portion of information may be whetherthe skid steer has advanced auxiliary hydraulics or not which may beused to identify hydraulic power output). In another example, tangentialinformation for the second portion of information may refer to moredirect information (e.g., second portion of information may be hydraulicpower output identification number or some other identification numberlinked directly to hydraulic power output). As yet another example, thesecond informational item may be the transmission type of a car (e.g.,standard or manual), and the second portion of information may bewhether the car has a clutch or not which may be used to identify thetransmission type.

In some implementations, an alternative solution path may cause queryprocess 10 to try to identify the appropriate component (or part) basedon the intended usage, in which case, determining the usage may becomerelevant (e.g., farming). Thus, some required informational items may bedetermined based solely on the intent, while other informational itemsmay only become clear based on the means selected to satisfy thatintent.

In some implementations, query process 10 may generate 308 a question todetermine the second portion of information based upon, at least inpart, the second portion of information being unknown. For example, insome implementations, the question may be one of a direct question andan indirect question to determine the second portion of information. Forinstance, and continuing with the above example, assuming the secondportion of information (e.g., the information needed to answer thequestion(s) of which tires are compatible with the tractor) is unknown,query process 10 may generate 308 a question to determine thisinformation. In some implementations, the question may be a directquestion generated and provided to the user (e.g., “what are the exacttires that you need?” or “what types of tires fit your tractorconnector?”) . The question is a direct question because the answerbeing sought is the answer to the second informational item (e.g., whichtires are compatible with the tractor) without requiring an inference.In some implementations, the question may be an indirect questiongenerated and provided to the user (e.g., “what is the make/model ofyour tractor?”, “what is the Vehicle Identification Number of yourtractor?” or “what is the type of connector used between your tractortires and the tractor?”). The question is an indirect question becausethe answer being sought is actually to a different question than thesecond information item (which tires are compatible with the tractor),but it may be used to determine by inference (e.g., indirectly) thesecond informational item. For example, by knowing the type of connectorbetween tires and tractor, the domain assistant (e.g., via query process10) may be able to identify or determine which tires may be compatiblewith tractor. That is, if the user does not know the answer to thedirect question of which tires are compatible with the tractor, queryprocess 10 may generate and ask tangential questions to get the neededinformation. For example, if the question is “what is the make/model ofyour tractor?” or “what is the Vehicle Identification Number of yourtractor?” or “what is the type of connector used between your tractortires and the tractor?” query process 10 may request information fromthe KB that may have a table detailing all the compatible component orparts with a particular make/model of a tractor or its VIN or its typeof connector. As such, by asking the particular make/model of a tractoror its VIN or type of connector, query process 10 may indirectly obtainthe answer for the second informational item (which tires are compatiblewith the tractor). Therefore, query process 10 may determine what neededinformation is missing. Then, ask questions to obtain unknowninformation (directly or indirectly).

In some implementations, query process 10 may receive 310 an answer forthe question generated to determine the second portion of information.For instance, query process 10 (e.g., via UI 500) may generate andprovide the question to the user, and the user may use UI 500 to respondto the question, which may be received by query process 10.

In some implementations, query process 10 may determine 314 a sequenceof a plurality of questions to be generated. For example, rather thanhaving a rigid conversational workflow, where the same questions areprovided to the user in the same specific order to get to the answer,query process 10 may constantly optimize the questions and their orderof being provided to minimize what it has available to ask the user.Query process 10 may constantly look for opportunities (e.g., bychecking existing records and other information) to avoid having to askthe user questions. That is, unlike some Q&A systems, query process 10does not use a hard-coded path to a result where a fixed set of tasksare used for a set of fairly rigid conversational workflows to interactwith the user to collect needed information and to present results.Instead, query process 10 may look to the KB or other informationsources to determine what next question should be asked as it looks atdata. This is dynamic in that as data changes, questions may be adaptedand thus dialogue with user may be adapted. In some implementations,query process 10 may leverage knowledge of the domain, the users, andcontext to flexibly and dynamically generate a suite of conversationalworkflows (at application configuration time) and to modify the paththrough them at run-time. This may provide a customized and streamlineduser experience, avoiding unnecessary interactions and allowing foralternative paths to a solution.

In some implementations, query process 10 may utilize generation ofalternative paths based on domain and world knowledge, which may resultin a different sequence of questions being generated and provided to theuser. As an example, the specific model number for a system componentmay be determined by the system into which it fits, by the component towhich it connects, by the intended usage of that component, or by thecomponents previously ordered by a given customer. Using this knowledge,a workflow may be created that contains all of these alternative paths.Query process 10 may then, at run time, prefer one path over anotherpath based on the ease with which the necessary constraining informationmay be obtained, the likelihood that the user may know certain thingsbut not others, a particular path having fewer questions than anotherpath, or other heuristics.

In some implementations, the sequence of the plurality of questions tobe generated may be determined 314 based upon, at least in part, one ofuser information and context. For instance, query process 10 may takecontext (and possibly history of what user has asked before, otherinformation about user, etc.) to come up with next question to ask(rather than step-by-step question/answer on a track). In someimplementations, if certain information has already been provided, thedomain assistant may follow the path that avoids the need to request anyadditional information from the user.

In some implementations, regardless of how the informational needs areidentified, query process 10 may also determine how it may acquire thenecessary information (i.e., the informational items). This may includeasking the user, querying data sources, invoking other services, orseeing if it is available in the current context. An example ofcontext-provided information may include information provided as part ofthe prior conversation with the user (e.g., “I want to buy a heavy-dutybucket for some construction work” contains information about the bucketdurability as well as the intended usage). Information about the user’srole, location, organization, etc. may also serve to provide the neededinformation. For instance, the user’s current location may be used asthe default origination city when booking a flight.

In some implementations, given multiple sources of information, queryprocess 10 may determine the “best” way to obtain that information,taking various factors into account including, but not limited to, cost(in terms of financial cost, time, convenience, etc.), speed, accuracy,likelihood of the source having the information, etc. For example, if anequipment’s serial number is needed and query process 10 may find it onan invoice or ask the user, it may recognize that looking up the invoicewould be both more convenient (i.e., not bothering to ask the user ifnot needed) and more likely to yield an accurate answer. Where queryprocess 10 may have access to information that is not definitive orwhere various conflicting information is available, query process 10 mayask the user to select or confirm the information retrieved, which maybe simpler for the user than having to provide the information fromscratch.

In some implementations, determining 314 the sequence of the pluralityof questions to be generated may include excluding 316 generation of asecond question for determining the first portion of information. Forexample, query process 10 may be dynamically optimized such that queryprocess 10 determines questions needed and the sequence of questions tobe asked (e.g., minimizing the number of questions asked) based oncontext, understanding, and probability that the information is going tobe needed. For example, where a standard workflow may normally includesteps 1-8 (where each step has a new question), instead of walkingthrough each question in order, query process 10 may jump down toquestions 7 and 8 that need to be asked based on system already havinganswers (from knowledge) to questions (corresponding with steps 1-6)that meet probabilities.

In some implementations, query process 10 may be optimized for best userexperience. This may imply aiming at reaching their goal with a minimalworkflow. For instance, when dealing with common vs. exception-casescenarios, several strategies may be adopted. For example, assuming thata service needs to provide answers:

-   1) Workflows may be extended to gather additional information before    providing a final unique answer; or-   2) Several answers may be given together with conditions under which    they apply.

Query process 10 may be able to evaluate and choose between options 1)and 2) depending on several factors, including, e.g., probability ofexception-cases, consequences of missing the correct answer, userpreference, etc. Thus, if query process 10 understands knowledge of theuser and intent of the user, then query process 10 may determine whatinformation is needed to accomplish goal of the user (related to theirintent) through iterations. Based upon context, intent, and what comesback from the user may be used by query process 10 to determine whetherto proceed with follow-up questions. For instance, information fidelityissues may be optimized (e.g., how much information to include), e.g.,regarding buckets that work with European power vs. buckets that workwith American power, instead of listing all these buckets, query process10 may present the user (e.g., via UI 500) with compatible and relevantbuckets based on knowledge of user being in America and user system.

In some implementations, the cost of obtaining certain information,especially but not exclusively from the user, may potentially exceedbenefit of making a request for it. In such cases, it may be appropriateto default to typical values for that information and present thatassumption to the user along with results arising from it. As anexample, a certain component may be compatible with all models of asystem (e.g., equipment) of a certain type except for one rarelyoccurring configuration. Rather than asking each user or otherwisetrying to determine whether query process 10 has access to that unusualconfiguration, query process 10 may elect to assume that is not thecase, provide the user with the appropriate results (which excludes thatunusual configuration), and include a caveat that this answer is notvalid if they indeed have that specific configuration. In example caseswhere query process 10 may be able to confirm whether the exceptionalbehavior holds, query process 10 may still elect to only validate thisas a last step, thus avoiding complications within the more commonworkflow paths. For instance, if a certain component is not certifiedfor use in California, the configuration service may proceed along thedefault paths and, as a final step, check the shipping location of theproduct and flag it if the assumptions made are violated (incorrect).

Query process 10 may provide 312 a response to the intent relating tothe query based upon, at least in part, at least the first informationalitem for which the first portion of information is already known and theanswer for the question generated to determine the second portion ofinformation. For instance, as noted above, query process 10 may use UI500 to assist the user through the workflow to fulfill their goal andobtain the required information. As such, based upon already knowing thefirst portion of information (e.g., engine type) thus knowing firstinformational item (e.g., type of tractor) and the answer to the secondportion of information needed to answer second informational item (e.g.,the second portion of information needed to answer the question(s) ofwhich tires are compatible with the tractor, such second portion ofinformation may be type of connector between tires and tractor,make/model of the tractor), query process 10 may optimize the workflowto determine which questions are necessary to ask the user, and whichmay be skipped or retrieved by query process 10 without asking the user,and then provide a response to the intent relating the query (e.g., thedesire to purchase new tires for a particular tractor), which mayinclude the specific tires needed for the user’s tractor, the ability topurchase the tires (e.g., via UI 500 or another process/website, etc.).

Thus, as described throughout, one example goal of query process 10 maybe to recognize the user’s intent, and then to invoke the appropriateaction (e.g., execute a service) or information request and provide anappropriate response. How the intent is interpreted, what the correctaction or query to invoke is, what the response should be, and how theresponse should be presented may all vary based on the user, the currenttask context, the discourse context, and world state. In someimplementations, if query process 10 has a model of these intents,actions, users, and domains, it may modify its behavior and/or workflowaccordingly. For example, the same user utterance (speech, text, orother interactions) may be interpreted to mean different intents indifferent contexts. The same intent may be satisfied via differentactions or queries in different contexts. The same query may result indifferent responses and each response may be presented differently indifferent contexts through use of query process 10. This ability to usemodeled knowledge to customize query process 10′s behavior may be oneexample characteristic that may distinguish it from simpleraction/response chatbots.

In some implementations, query process 10 may identify 318 a service fora second object associated with the object and query process 10 maygenerate 320 one or more additional questions associated with theservice. For instance, query process 10 may determine whether otherservices are applicable based on the above-noted response. For example,since query process 10 is now aware that the user has a tractor, queryprocess 10 may identify 318 a particular service (e.g., insuranceservice) that may be useful to the user. For instance, in the example,assuming the user intent/goal is to buy a component (e.g., tires) forthe tractor, query process 10 may run a configurator service (e.g., findthe correct component), which may be one of several services that queryprocess 10 may select. After query process 10 fulfills the originalintent goal by running the configurator service, query process 10 maydetermine what other services may be useful and relevant to user. Forexample, query process 10 may help user buy the tires, and then queryprocess 10 may also determine that user could use another service suchas an “insurance” service for extending their insurance (e.g.,generating and providing additional questions such as, “do you want toextend insurance?”). Another example service may be a maintenancemonitoring service, a recommendation sales service (e.g., recommendationfor increasing sales), etc. Whether or not query process 10 triggers aservice may be based on information encoded in the above-noted database(or otherwise) and based on user information (e.g., user preferences orcontext in which user is using application). Query process 10 may beencoded such that several services may be linked together and triggered(e.g., when the insurance service is triggered the maintenancemonitoring service may also be triggered).

In some implementations, as described above, query process 10 may selectan appropriate service to satisfy the user’s intent. In this example,the configurator service may be selected as the appropriate service. Theconfiguration service (also referred to as component selection service,configurator, or component selector) may be a software application thatmay assist with identifying/selecting a component that belongs to and iscompatible with a tractor (or other system) and its possibleconfigurations. This software application (which may be a component ofquery process 10) may provide a query or may be part of a separateservice-specific query sub-process service (e.g., configuration querysub-process). Generally, the configurator service may be responsible forunderstanding and assisting processes centered around searching througha potential set of components and finding the best component thatmatches a user’s intent, when there exist the example elements of:

-   Search results may be compatible with the user’s system of interest;-   User’s intended use may restrict potential matches;-   User may have further conditions to place upon the search results;    and/or-   Knowledge model may include various types of information to support    the assessment of system/component compatibility.

These categories of information are shown in the table below and mayinclude, e.g.,:

System and Component Class Organization Systems may be organized intohierarchies that may be functional or based on marketing need (meaningthe organization of the classes may not be ontologically logical butserve a marketing vs a functional goal). A user may specify things atany level of specificity, so query process 10 may need to be able toadapt to the user’s description if possible, as the user may reference amarketing understanding of the system as opposed to its correcttechnical term. Components (e.g., parts) may be similarly organized intotheir own hierarchies. Examples may include, e.g.,: Engine Parts,Buckets, Worktools, Accessories, Drive Train, Memory Systems, etc.System and Component Identifiers Systems and components typically have astandard or typical nomenclature for referencing them. For vehicles,this may be Make/Model/Year. For Heavy Equipment this may be a modelnumber, or family of equipment. Multiple naming methods may exist (e.g.,model number, family, generation, product family, etc.) A naming systemfor components may be scoped to the component class. Examples mayinclude: High Octane as a fuel type, a Heavy Duty Bucket as a type ofbucket, etc. System and Component Specification Models Variousproperties may be used to describe specific classes of systems orcomponents. These may apply to general categories of components, e.g.,all physical objects may have a width, height, weight, etc. or toprogressively more specific classes. Manufactured objects may have amanufacture, SKU, list price, etc.Computers may have a specific chipset,screen resolution, etc. These system/component properties provide fodderfor compatibility decisions. Compatibility Models System/componentcompatibility may be specified explicitly (e.g., as a table of whatcomponents work with which systems), via constraints on propertiesspecified in the System/Component Specification Models (e.g., a clutchmay be dependent upon a car’s Transmission Ring Gear Count and theShifter Placement Style), or via real-world constraints (e.g., awater-craft may not be appropriate for a land-based operation). TheCompatibility Models are where such constraints may be defined.

Once a user’s intent, or an opportunity to proactively contact the userhas been determined by query process 10 to require identification ofcompatible components to the user, query process 10 may have a processfor determining how to pick which components are valid ones to provide.For example, in order to state the compatibility question formally,whether issued interactively or determined proactively, the followingexample information types to break down the query in the table below maybe declared:

Info Type Meaning Examples System Identifier Which system are we talkingabout? Within a domain this identifies a specific system instance ortype. Microsoft, Excavator, Bull Dozer, Car, Ford Mustang, Ford Mustang1977, Internet Information Server, MySQL System Identification TypeSystem Identifiers are usually typed in some way; We know that“Microsoft” is a Company Name, “Ford Mustang” is Model Number,Year/Make/Model, Product Number, “Make/Model”. Systems may havedifferent ways to identify them, so System Identification types andSystems are not necessarily one to one. Product Class, Product Type,Equipment Type, Plan Name, Product Name System Descriptor(s) OptionalInformation that may help, after knowing the system, pick from thepossible instantiations of this system in a meaningful way. A namedconfiguration such as C-Corp/LLC, or Heavy Duty, “Lowered” (Referring toSuspension) Component Identifier Identify which, of the possiblecomponents (or component types) trying to be located. It may be anindicator of what the user is “looking for” that may be usable with whatthe user “has” (their system) Engine, Plugin, DLL, Transmission Fuel,Clutch Disk Component Descriptor(s) Further descriptive informationthat, once some or all components are known that match the identifier,will help indicate the one being looked for. These are generalproperties/classifications to help talk about/identify componentsdescriptively. Carbureted

Examples: “I’m looking for Organic Clutch Disks for my Ford Mustang1977,” “I have a heavy-duty jack hammer, do you have any 6″ extensionbits?” “I need a standard duty bucket for my 505E long reach.”

Other Examples of Components/System Relationships may include, e.g.,:Tires (components) for Cars (systems), Buckets (components) for HeavyEquipment (systems), Valves (components) for Hydraulic Pum+ps (systems),etc. The component the user is looking for may be in this top-levelpartition. Components may serve many purposes, e.g., be upgrades,original equipment manufacturer (OEM) replacements, accessories, etc.

In some implementations, and referring at least to the exampleimplementation of FIG. 6 , given a representation of the possible spaceof systems represented as Classes, by way of example, a sample graph 600representing Heavy Equipment (Machines) is provided, e.g., a hierarchyof Machine types.

In some implementations, and referring at least to the exampleimplementation of FIG. 7 , of the potential components of these systems,the configurator service (which may be executed by at least a portion ofquery process 10) may focus on Worktools as shown in the sample graph700. In the example, a user may seek individual, specific, tools (e.g.,buckets) to use with their machine.

Referring at least to the example implementation of FIG. 8 , an exampleflowchart executed by query process 10 that may be used to guide theuser and optimize the questions needed to be presented to the user isshown. As noted above, query process 10 may try to produce results assoon as possible and only ask questions if it fundamentally changes theresults.

Phase 1 - Identify Which Class Compatibility Model Being Operating Under

As the Class Compatibility Model (e.g., via query process 10) determineswhether the pairing is even possible (e.g., whether there exists acomponent compatible with the described system), query process 10 mayneed to find the best model that matches the users request. In someexamples, the user’s request may not be possible: e.g., “Are there anyexcavators at Microsoft that know Java?”

Step 1 - From User

As noted above, the relevant system of interest may be explicitly statedin the query or extracted from the user information in the knowledgegraph, a local database, or in another data source.

Step 2 - Lookup System

In some implementations, the user’s system descriptor may need to beclassified, labelled with the most specific class to which thedescriptor belongs. This may be done through, e.g., natural languageprocessing (NLP) training, lookup in a local/remote database, or cachedin the Knowledge Graph/Knowledge Base. The Class node may need to belocated with the Knowledge Graph for the algorithm to continue, as thedomain assistant may operate on these relationships to proceed.

Step 3 - Lookup Component

Component lookup may follow the same concept as System lookup. Queryprocess 10 may proceed if the Component Class is identifiable, withinthe context of the System in case the identifier is ambiguous.

Step 4 - Collect Class Compatibility Models

Next, query process 10 may produce a set of some or all CompatibilityModel Nodes (or Facts) that represent the existence of compatibilitydata between two classes and the conditions upon which compatibilitymust be evaluated. For example, and referring at least to the examplegraph 900 of FIG. 9 , the search for “Worktools for my Machine” has fourClass Compatibility nodes that link to descendants of Worktool andMachine.

Step 5 - Validate Anchor Compatibility

By convention, top level compatibility may be encoded into a model(e.g., as a class general statement). This may refer to class statementssuch as “Machines take Worktools”, etc. If such a model includes theseAnchor statements, query process 10 may validate this as part of thealgorithm. This may allow rejection of such statements as “Which Engineshave Employees?” as being semantically incorrect.

Step 6 - Validate Best Compatibility Model Fits System

Query process 10 may look to a candidate set for nodes related to theSystem identifier first. If a direct relationship is not found, but oneis found in the descendants, query process 10 may ask the user toclarify (step X1). If a system match is found, then process initiatesvalidation of component compatibility at step 7.

Example: Mini Hex -> Bucket, No issue, go to Step 7.

Example: Machine -> Hammer, No Issue, go to Step 7.

Example: Machine -> Bucket, Must clarify Machine Class descendant, go tostep X1.

Step X1 - Ask User

Referring at least to the example graph 1000, the user may be presentedwith potential descendants visually, but may accept any descriptor inthe tree and then may return to step 1 to re-evaluate the query. Step X1may involve asking of user which system sub class is intended.

Step 7 - Validate Best Compatibility Model Fits Component

Step 7 may involve same process as Step 6, but following the Componentside. Step 7 may provide validation of component compatibility in termsof validating best compatibility model for fitting component. If nomatch, then process initiates step X2. If match, then process startsphase 2.

Step X2 - Ask User

Same process as step X1 but instead relates to component instead ofsystem. Step X2 may involve asking of user which component sub class isintended. Again, referring at least to the example graph 1000, the usermay be presented with potential descendants visually, but may accept anydescriptor in the tree and then may return to step 1 to re-evaluate thequery.

Phase 2 - Utilize Class Compatibility Model

A class compatibility model may relate to defining that two classes havea compatibility relationship and describe what factors match betweenSystem and Component to drive compatibility. Another incarnation of thisconcept is the schema for compatibility nodes within a graph. From thecompatibility model, query process 10 may determine which descriptors(system or component) may need to be inquired upon. The example graph1100 of FIG. 11 provides annotations by way of explanation:

Mini-Excavator Buckets depend on Machine Weight.

Excavator Buckets Depend on Linkage and Bucket Weight/Capacity Comparedto Lift Capacity of Machine.

Backhoe Front Buckets depend on the Tool Carrier Type (e.g., Feature).

Backhoe Back Bucket depends on Model Number.

In some implementations, the sequence of the plurality of questions tobe generated may be determined 314 based upon, at least in part, aprobability that the second portion of information is needed to providethe response to the query. For example, annotations in the graphregarding what the user may or may not know may allow an adaptive model(e.g., via query process 10) to ask indirect questions in satisfactionof this goal. For example, asking the user for a “machine Weight” may bemarked as a low probability field, but asking for a model number, whichis highly knowable, may provide the domain assistant the “machineweight” directly. In addition, from a possible set of directcompatibility relations and inferred alternative means of satisfyingthese relations, the adaptive domain assistant system may decide to takea known factor and instead of asking the user, may merely note it.

For example, a compatibility factor of “stick length” may affect lessthan 5% of the components (meaning it only makes a difference in 5% ofthe components that would match if you relax this constraint) so insteadof asking the user “stick length”, query process 10 may note therequirement on matching parts.

For example, some exhaust parts do not comply with Californiaregulations. Rather than asking every user if they reside in California(assuming this isn’t known to query process 10), query process 10 mayevaluate that California is just 1 of 50 states so has a low probabilityof being relevant. Instead the state regulation warning may become anannotation rather than a requirement.

In general, from a population of possible components (e.g., Buckets orClutches), there are those that are “compatible” with specific systems(e.g., machines), but it is usually an indirect relationship. In someexamples, relationship may be simplified to whether a component may workwith system (e.g., bucket X (component) may work with machine Y(system)).

As another example, a clutch (component) may be compatible with a 1967Shelby Mustang (system) based on one or more of the following fourexample elements that drive compatibility:

-   The number of ring gear teeth;-   The spline size;-   The horsepower requirement; and/or-   The Placement of the shifter;-   Thus, for a clutch (component) to be compatible with a car (system),    at least one or more of the following example four elements should    be met:-   It should have the matching number of ring gear teeth;-   It should match the spline size;-   Its engine should not exceed the horsepower requirement; and/or-   It should have any special shifter requirements.

In another example, instead of asking a question, since query process 10may know information is needed in the selection criteria, query process10 may decide whether or not a question subdivides a set large enough towarrant the risk of asking a question. For example, query process 10 mayask or provide a table with a conditional item depending on risks basedon percentage of exception and consequence of the exception.

It will be appreciated that in some implementations, query process 10may generate 308 the answer for the question from a virtual agent. Forexample, similar to a “Virtual Sales Assistant for Component Selection”interface, query process 10 may be executed using virtual agenttechnology. The user interface of this system (e.g., UI 500) may beconversational in nature and may demonstrate component selection,content navigation and delivery, sales ordering, and reporting. Theexample virtual agent may be designed to be scalable over time as moreparts (e.g., hammers), new machine models, additional content, andadditional dealers are added, to serve as the most efficient andeffective Virtual Sales Assistant (or other interactive interface). Theexample virtual agent may be hosted securely on other web services, mayuse an English-language only (or other language), may use conversational(combination of machine learning, natural dialog and navigational hints,etc.) user interface, may allow for faster and correct componentselection for the list of company provided makes/models of the excavatorproduct family (e.g., 123A B, 456A B, 123C D, 456C D, 789E F, etc.), mayenable making recommendations, may place an order and/or capture thelead for selected component through a sales representative, may reportsales of components (e.g., buckets), and may publish field sales data toa company designated web portal (or otherwise), discovering, navigating,and/or delivering relevant content from company provided content,including technical description, marketing brochures, and commercialtraining, identifying what content would be public, and what would begated and the appropriate delivery mechanisms, identifying coachingneeds for sales reps based on, e.g., testing using a select number ofconversations/component-selection flows, controlling data access (e.g.,provisioning and revocation may comply with security specifications),generating usage data, including for example, a daily performancereport, which may be analyzed and utilized to understand its salesstrategy, Critical Success Factors (CSFs), Key Performance Indicators(KPIs) and customer buying behavior, performance data, history ofquestions asked by a user, answers provided, navigational decisions byquestions, log data including, e.g., where and when accessed, andsession length, providing voice support under mobile native application.

In some implementations, the query process 10 may be integrated with anexample domain assistant system. The domain assistant system mayidentify an intent relating to a user’s goal based on events (e.g., userrequests, database triggering event based on criteria, etc.). The domainassistant system may then determine at least one service relating to thegoal/intent and then run or execute the service(s) to fulfill thegoal/intent.

As described above, the domain assistant system may utilize virtualagent technology to accomplish objectives (e.g., fulfill goals/intents)through the following services:

The configuration service (also referred to as component selectionservice, configurator, or component selector) may be a softwareapplication that can assist with identifying/selecting a component thatbelongs to and is compatible with a system and its possibleconfigurations. Component Selection may include providing a multi-stepprocess for identifying the compatible components (e.g., attachments)that will fit into a system with a specific configuration.

This can be accomplished when the user describes the system in questionor has the system work with the user to extract system specificationsneeded in order to narrow the possible components available to thosethat are compatible with the system in its described configuration.

The Sales Representative Training Service may provide ability totrain/test knowledge of sales representatives on selling components(e.g., buckets). The Sales Representative Training Service may providemeans to discover and access appropriate technical information,marketing, and commercial training materials (e.g., train sales based onmultiple files). The Sales Representative Training Service may providemotivational content and recommendations for increasing sales ofcomponents (e.g., buckets), allow faster and correct component selectionfor list of company provided production models (e.g., excavator models),capture lead and place order for selected component through salesrepresentative, and report sales of components to a spreadsheet orcompany provided system.

The user interface for the domain assistant system may be conversationalin nature and may demonstrate component selection (e.g., forconfigurator service) as well as delivery of supportive services such ascontent navigation & delivery, sales ordering, and reporting. Somefunctionalities of this system may include guiding users through theconfiguration service process, taking voice notes, encoding customers’answers via clicks, and converting all relevant information into facts.Two scenarios that may considered for this invention include:

Live: Sales staff may use the application of the domain assistant whileon the phone with a potential customer or in person; and

Offline: All the details about customer preferences/job requirements arealready available/known to user who can use the application without thecustomer present to generate a proposal.

The domain assistant may be able to understand multiple intents that maybe understood simultaneously. The domain assistant may generate usagedata that may include a daily performance report that can be analyzed &utilized to understand its sales strategy, customer buying behavior,etc. The domain assistant may be used to obtain pricing and inventoryinformation for products as well as supplement information from cachedand/or remote sources.

The domain assistant may include a query engine (e.g., conversationquery manager (CQM)). The domain assistant may provide a combination ofinputs (e.g., selection) by way of voice, click, swipe, text chat, etc.The domain assistant may allow you to start and stop dialogue atdifferent stages and then come back to these same dialogues at a futuretime.

Live: Sales staff may use the application of the domain assistant whileon the phone with a potential customer or in person; and

Offline: All the details about customer preferences/job requirements arealready available/known to user who can use the application without thecustomer present to generate a proposal.

The domain assistant may be able to understand multiple intents that maybe understood simultaneously. The domain assistant may generate usagedata that may include a daily performance report that can be analyzed &utilized to understand its sales strategy, customer buying behavior,etc. The domain assistant may be used to obtain pricing and inventoryinformation for products as well as supplement information from cachedand/or remote sources.

The domain assistant may include a query engine (e.g., conversationquery manager (CQM)). The domain assistant may provide a combination ofinputs (e.g., selection) by way of voice, click, swipe, text chat, etc.The domain assistant may allow you to start and stop dialogue atdifferent stages and then come back to these same dialogues at a futuretime.

A domain assistant may use query process 10 (and/or may be a componentof query process 10) that may be applied to a variety of fields andrelate to various value propositions. In some implementations, andreferring at least to the example implementation of FIG. 12 , an exampleworkflow 1200 of an example domain assistant is shown that may be usedby query process 10.:

At steps 1 and 2, domain assistant identifies intent and subject system(e.g., machine) from intent e.g., domain assistant identifies intentsuch as what is machine (step 1) and what is user trying to do (step 2).Then, domain assistant may go to next steps in process. Steps 1 and 2may be used to identify what user is talking about topically and valueproposition user is trying to accomplish.

At step 3, domain assistant may determine one or more services tosuggest to user with respect to intent and then proceed to execute oneor more of the services that user agrees to being executed (e.g.,execution of one or more of following steps triggering services: step 4(kits service), step 5 (configurator or parts service), step 6 (toolsservice -give user tools), and step 7 (instructions service - give userinstructions).

In summary, the domain assistant system may determine what is theproblem, what is the value proposition, and then the system maycommunicate what to do (e.g., here is what user needs to buy, know, orobtain). System provides total solution for user.

For example, there is a need to disassemble engine ➔ domain assistantmay provide instructions, tools, parts (which may all be sold to user)regarding disassembly of engine. In another example, machine is broken(step 1 - system identified as machine) and domain assistant mayidentify from intent what user is trying to do (step 2). Then, domainassistant may respond with what should be done in terms of service(s)(step 3) e.g., what may be available in terms of kits (step 4) and thenmay get user the parts/tools/instructions (via steps 5-7).

Domain assistant system may also provide references at step 8 (e.g.,videos, tips, read up on it, people that did this process also foundthis process useful, here is what our top experts say about issue, otherpeople with your problem have also found X helpful, etc.).

As an end-to-end, this example methodology provides a workflow that mayapplied to any component (e.g., equipment) or any system (e.g.,machine).

In an example, domain assistant system may identify an intent by e.g.,determining a problem (1) and determining a user goal (2). Domainassistant may then determine what to do (step 3) and then may executeone or more of the following services as shown in flow chart above: findrelevant kits (step 4), find relevant parts (step 5), find relevanttools (step 6), find relevant instructions (step 7), and/or findrelevant references (step 8). Providing customer a part or component maynot be sufficient in satisfying all of the user’s goals. Thus, othertangential services may be initiated such as services at step 7 and step8 (e.g., user may need instructions and references as well related topart or component identified in configuration service). Later steps(i.e., steps 4-8) are examples of services that can be executed to helpuser that may cover a variety of actions. Domain assistant may providedata management & availability such as query technical information andpublications (both structured and unstructured data).

Below is a summary of each step with examples:

Step 1. What Machine? (e.g., What Is It? or What Are You Fixing?)

Example Answers may include but are not limited to following: Model ofmachine ➔ provided by user making the inquiry, Serial # of machine,Description of machine e.g. compact track loader, HP printer, etc.

Step 2. What is the user’s goal? (e.g., what is user trying to do?) Thegoal may relate to fulfilling a specific service (e.g., configurationservice). The goal may be determined from goal-oriented dialogs and/orfrom other user information. Goal categories may include but are notlimited to following examples: sales opportunity (e.g., trying to buy apart), customer engagement tool (e.g., trying to find out that userequipment is going to break such that a notification from a predictiveengine states that the probability of the machine breaking is high anduser should maintain it), retail (e.g., user on a website that wants tobuy equipment to build airplanes, customer that wants to buy a bucket,etc.), Do-It- Yourself (DIY) (e.g., trying to change your printer ink),etc.. Goal may be determined from value proposition (e.g., context).Examples of value propositions that may be provided through a servicemay include but are not limited to the following:

-   Repair Indicator (need repair)➔ may be pulled from company data.-   Insurance (need to get component or device certified)➔ may be pulled    from data of Business Unit, Sales Team, Financial Services, etc.-   Certified Product ➔ may be pulled from data of sales opportunity-   Preventive Maintenance➔ may be pulled from data of company platform    (e.g., Symphony platform)-   Responding to sale/promotion➔ may be pulled from data of business    unit, sales representatives, retail, sales team, etc.-   New sale (e.g., buy new machine)➔ may be pulled from data of dealer    representative selling parts or attachments to fix a problem-   Installation e.g., part arrived but user does not know how to    install it-   Compatibility Check e.g., trying to determine if user has the    compatible part to do a repair

Step 3. What Services to Run or Execute

Based on goal within user intent, domain assistant may identify whichservice or services should be executed to accomplish goal. Then, steps4-8 may be selected accordingly.

Step 4. Find and Determine Relevant Kits Service

Example kits may include but are not limited to: Foundational Kits (TopEnd Kits, Major Overhaul Kits), procedure kits, parts kits, etc. Kitsservice may provide a prefabricated solution that can be bundled orcombined. Kits service may give instructions and internet data. Otherexamples of kit service may include but are not limited to following:Marketing Promo’s (e.g., kits written by someone in marketing),dealer-defined kits, kits enabled in a parts website, etc.

Step 5. Find and Determine Relevant Configuration Service (e.g., PartsService)

The configuration service (e.g., parts service) may list price, dealerprice, inventory, shipping, promotion, on sale, etc. Examples of partsservice may include but are not limited to the following: compatibleparts lists from a company team, original parts list from organization,builder (mined) for incomplete compatible parts, Dealer Database (needsintegration model) for incomplete compatible parts, company partswebsite, etc.

Step 6. Find and Determine Relevant Tools Service

Examples may include but are not limited to the following: Builder-defined tools (e.g., manually created) from company team, Parts or toolslisted on company website, etc.

Step 7. Find and Determine Relevant Instructions Service

Examples may include but are not limited to the following: ApprovedCompany Instructions ➔ e.g., derived from search or query and becomes afixed formula for this particular service, Dealer technician Notes e.g.,based on best search results, etc.

Step 8. Find and Determine Relevant References Service

Examples may include but are not limited to the following: Videos, Tips,Best search results, Advice/Best Practices, etc.

For instance, assume for example purposes only that an HP printer is outof ink. In this example, the domain assistant system may respond withprocedures (e.g., check to see if it is plugged in, run ink test to seewhat colors are low) or domain assistant may respond with diagnostic ordomain assistant may tell user about performance statistics, etc. Domainassistant may also assist with fixing a printer by providing executingservices related to parts, instructions, etc.

As another instance, assume for example purposes only that a hydraulicexcavator is the machine the user needs to excavate a mountain. Domainassistant system may ask user questions about what type of material(e.g., is it iron ore or dirt?). For example, if iron ore is selected,domain assistant may determine components that can be used forexcavating iron ore from mountain and then offer and sell thesecomponents to user.

As another instance, assume for example purposes only that in order toobtain insurance, component or machine may need to be company certified.This service may be used to for determining how to get machinecertified. For example, the machine may have to comply withcertification rules (e.g., need to have bought right parts, need to havefollowed official instructions each time you changed the oil, cannothave non-company parts, etc.) otherwise not eligible. Domain assistantmay use insurance service to help user obtain applicable insurance byguiding user.

As another instance, assume for example purposes only that there is apreventative maintenance service. There may be data sources within acompany or organization (e.g., enterprise data) that can be used bypreventative maintenance service in providing preventative maintenanceinformation. For example, user has hydraulic excavator and is concernedabout preventative maintenance. The domain assistant may usepreventative maintenance service to monitor company databases and theninstruct user accordingly (e.g., give user procedures and then tell userwhat to do next in terms of maintenance through life of vehicle).

The terminology used herein is for the purpose of describing particularimplementations only and is not intended to be limiting of thedisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. As used herein, the language “at least one of A, B,and C” (and the like) should be interpreted as covering only A, only B,only C, or any combination of the three, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps (notnecessarily in a particular order), operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps (not necessarily in a particular order),operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents (e.g., ofall means or step plus function elements) that may be in the claimsbelow are intended to include any structure, material, or act forperforming the function in combination with other claimed elements asspecifically claimed. The description of the present disclosure has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the disclosure in the formdisclosed. Many modifications, variations, substitutions, and anycombinations thereof will be apparent to those of ordinary skill in theart without departing from the scope and spirit of the disclosure. Theimplementation(s) were chosen and described in order to explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various implementation(s) with various modifications and/or anycombinations of implementation(s) as are suited to the particular usecontemplated.

Having thus described the disclosure of the present application indetail and by reference to implementation(s) thereof, it will beapparent that modifications, variations, and any combinations ofimplementation(s) (including any modifications, variations,substitutions, and combinations thereof) are possible without departingfrom the scope of the disclosure defined in the appended claims.

What is claimed is:
 1. A computer-implemented method comprising:identifying, by a computing device, an intent relating to a queryassociated with an object; identifying a plurality of informationalitems associated with the object; identifying at least a firstinformational item of the plurality of informational items associatedwith the object for which a first portion of information is alreadyknown; determining a sequence of a plurality of questions to begenerated for a conversational workflow, wherein determining thesequence of the plurality of questions to be generated includesexcluding generation of a second question for determining the firstportion of information; identifying at least a second informational itemof the plurality of informational items associated with the object forwhich a second portion of information is unknown; generating a questionto determine the second portion of information based upon, at least inpart, the second portion of information being unknown, wherein thequestion is part of the conversational workflow that is dynamicallygenerated based upon, at least in part, at least the secondinformational item; receiving an answer for the question generated todetermine the second portion of information; and providing a response tothe intent relating to the query based upon, at least in part, at leastthe first informational item for which the first portion of informationis already known and the answer for the question generated to determinethe second portion of information.
 2. The computer-implemented method ofclaim 1 wherein the answer for the question is generated from a virtualagent.
 3. The computer-implemented method of claim 1 wherein the secondportion of information is determined by inference based upon, at leastin part, the answer for the question.
 4. The computer-implemented methodof claim 1 wherein the sequence of the plurality of questions to begenerated is determined based upon, at least in part, one of userinformation and context.
 5. The computer-implemented method of claim 1wherein the sequence of the plurality of questions to be generated isdetermined based upon, at least in part, a probability that the secondportion of information is needed to provide the response to the query.6. The computer-implemented method of claim 1 wherein the second portionof information is absent from the answer for the question.
 7. Thecomputer-implemented method of claim 1 further comprising: identifying aservice for a second object associated with the object; and generatingone or more additional questions associated with the service.
 8. Thecomputer-implemented method of claim 1 wherein the question is one of adirect question and an indirect question to determine the second portionof information.
 9. A computer program product residing on a computerreadable storage medium having a plurality of instructions storedthereon which, when executed across one or more processors, causes atleast a portion of the one or more processors to perform operationscomprising: identifying an intent relating to a query associated with anobject; identifying a plurality of informational items associated withthe object; identifying at least a first informational item of theplurality of informational items associated with the object for which afirst portion of information is already known; determining a sequence ofa plurality of questions to be generated for a conversational workflow,wherein determining the sequence of the plurality of questions to begenerated includes excluding generation of a second question fordetermining the first portion of information; identifying at least asecond informational item of the plurality of informational itemsassociated with the object for which a second portion of information isunknown; generating a question to determine the second portion ofinformation based upon, at least in part, the second portion ofinformation being unknown, wherein the question is part of theconversational workflow that is dynamically generated based upon, atleast in part, at least the second informational item; receiving ananswer for the question generated to determine the second portion ofinformation; and providing a response to the intent relating to thequery based upon, at least in part, at least the first informationalitem for which the first portion of information is already known and theanswer for the question generated to determine the second portion ofinformation.
 10. The computer program product of claim 9 wherein thesecond portion of information is determined by inference based upon, atleast in part, the answer for the question.
 11. The computer programproduct of claim 9 wherein the sequence of the plurality of questions tobe generated is determined based upon, at least in part, one of userinformation, context, and a probability that the second portion ofinformation is needed to provide the response to the query.
 12. Thecomputer program product of claim 9 wherein the second portion ofinformation is absent from the answer for the question.
 13. The computerprogram product of claim 9 wherein the operations further comprise:identifying a service for a second object associated with the object;and generating one or more additional questions associated with theservice.
 14. The computer program product of claim 9 wherein thequestion is one of a direct question and an indirect question todetermine the second portion of information.
 15. A computing systemincluding one or more processors and one or more memories configured toperform operations comprising: identifying an intent relating to a queryassociated with an object; identifying a plurality of informationalitems associated with the object; identifying at least a firstinformational item of the plurality of informational items associatedwith the object for which a first portion of information is alreadyknown; determining a sequence of a plurality of questions to begenerated for a conversational workflow, wherein determining thesequence of the plurality of questions to be generated includesexcluding generation of a second question for determining the firstportion of information; identifying at least a second informational itemof the plurality of informational items associated with the object forwhich a second portion of information is unknown; generating a questionto determine the second portion of information based upon, at least inpart, the second portion of information being unknown, wherein thequestion is part of the conversational workflow that is dynamicallygenerated based upon, at least in part, at least the secondinformational item; receiving an answer for the question generated todetermine the second portion of information; and providing a response tothe intent relating to the query based upon, at least in part, at leastthe first informational item for which the first portion of informationis already known and the answer for the question generated to determinethe second portion of information.
 16. The computing system of claim 15wherein the second portion of information is determined by inferencebased upon, at least in part, the answer for the question.
 17. Thecomputing system of claim 15 wherein the sequence of the plurality ofquestions to be generated is determined based upon, at least in part,one of user information, context, and a probability that the secondportion of information is needed to provide the response to the query.18. The computing system of claim 15 wherein the second portion ofinformation is absent from the answer for the question.
 19. Thecomputing system of claim 15 wherein the operations further comprise:identifying a service for a second object associated with the object;and generating one or more additional questions associated with theservice.
 20. The computing system of claim 15 wherein the question isone of a direct question and an indirect question to determine thesecond portion of information.